Antiviral and Anti-Inflammatory Treatment with Multifunctional Alveolar Macrophage-Like Nanoparticles in a Surrogate Mouse Model of COVID-19

The pandemic of coronavirus disease 2019 (COVID-19) is continually worsening. Clinical treatment for COVID-19 remains primarily supportive with no specific medicines or regimens. Here, the development of multifunctional alveolar macrophage (AM)-like nanoparticles (NPs) with photothermal inactivation capability for COVID-19 treatment is reported. The NPs, made by wrapping polymeric cores with AM membranes, display the same surface receptors as AMs, including the coronavirus receptor and multiple cytokine receptors. By acting as AM decoys, the NPs block coronavirus from host cell entry and absorb various proinflammatory cytokines, thus achieving combined antiviral and anti-inflammatory treatment. To enhance the antiviral efficiency, an efficient photothermal material based on aggregation-induced emission luminogens is doped into the NPs for virus photothermal disruption under near-infrared (NIR) irradiation. In a surrogate mouse model of COVID-19 caused by murine coronavirus, treatment with multifunctional AM-like NPs with NIR irradiation decreases virus burden and cytokine levels, reduces lung damage and inflammation, and confers a significant survival advantage to the infected mice. Crucially, this therapeutic strategy may be clinically applied for the treatment of COVID-19 at early stage through atomization inhalation of the NPs followed by NIR irradiation of the respiratory tract, thus alleviating infection progression and reducing transmission risk.

Driving Forces of Changes in Air Quality during the COVID-19 Lockdown Period in the Yangtze River Delta Region, China

During the COVID-19 lockdown period (from January 23 to February 29, 2020), ambient PM2.5 concentrations in the Yangtze River Delta (YRD) region were observed to be much lower, while the maximum daily 8 h average (MDA8) O-3 concentrations became much higher compared to those before the lockdown (from January 1 to 22, 2020). Here, we show that emission reduction is the major driving force for the PM2.5 change, contributing to a PM2.5 decrease by 37% to 55% in the four YRD major cities (i.e., Shanghai, Hangzhou, Nanjing, and Hefei), but the MDA8 O-3 increase is driven by both emission reduction (29%-52%) and variation in meteorological conditions (17%-49%). Among all pollutants, reduction in emissions mainly of primary PM contributes to a PM2.5 decrease by 28% to 46%, and NOx emission reduction contributes 7% to 10%. Although NOx emission reduction dominates the MDA8 O-3 increase (38%-59%), volatile organic compounds (VOCs) emission reduction lead to a 5% to 9% MDA8 O-3 decrease. Increased O-3 promotes secondary aerosol formation and partially offsets the decrease of PM2.5 caused by the primary PM emission reductions. The results demonstrate that more coordinated air pollution control strategies are needed in YRD.

[Prediction of severe outcomes of patients with COVID-19].

Objective: To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients. Methods: Based on the patients' admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model. Results: In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94%, peripheral white blood cells count >8.0×10(9), change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7%, and the missed diagnosis rate was 38.3%. The prediction sensitivity of the model based on the test set was 75.0%, and the missed diagnosis rate was 25.0%. Conclusions: Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model's prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.